A memristor-based competitive decision neuromorphic circuit system
By designing a memristor-based competitive decision-making neuromorphic circuit system, the problems of lack of physical competition mechanism and dynamic time delay characteristics in the existing technology are solved. This enables multi-level evaluation of multimodal danger signals and biomimetic simulation of biological neural networks, thereby improving the intelligence and adaptability of the biomimetic system.
Patent Information
- Application Number
- CN202610388058.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-27
- Publication Date
- 2026-07-17
AI Technical Summary
Existing memristor-based decision-making or emotion generation circuits lack a real physical competition mechanism when dealing with complex multiple stimulus conflicts and behavioral pattern switching. They cannot simulate the competitive and smooth switching in biological neural networks, nor can they reflect dynamic time delay characteristics and long-term memory, which limits the ability of bionic circuits to achieve intelligent interaction and environmental adaptability.
A competitive decision-making neuromorphic circuit system based on memristors was designed, including a danger detection module, a mode switching module, and a feedback module. The system utilizes the resistance change of the memristor to simulate the competitive mechanism of biological neural networks. Dynamic decision-making and long-term memory are achieved through signal integration, multi-level comparison, and feedback modulation, simulating the adaptive behavior of organisms when facing complex environments.
It achieves multi-level fusion assessment of multimodal hazard signals, simulates competitive smooth switching and long-term memory in biological neural networks, improves the intelligence and adaptability of biomimetic systems in complex environments, and enhances rapid response and adaptive learning characteristics to environmental threats.
Smart Images
Figure CN122414282A_ABST